Senior Data Scientist – AI & Data Warehouse Systems

EXANTE·Serbia·Удалённо·1 мес. назад

We are looking for a Senior Data Scientist to help build the next generation of analytics on top of our enterprise data warehouse. This role is focused on a practical and increasingly important challenge: how AI interacts with structured business data.

The successful candidate will help design and build systems where AI can understand, query, validate, reconcile, and reason on analytical data in a reliable and controlled way.

This is not a traditional dashboarding role, and it is not a pure research-focused Data Science role. The primary focus is practical implementation of AI systems inside a modern data warehouse environment.

The role combines several disciplines:

Data Science
AI / Large Language Models (LLMs)
Data Warehousing & Analytics Engineering
Machine Learning
Data Quality & Validation
AI Agentic Workflows

We are building an AI-ready analytical environment on top of our data warehouse where AI can reliably interact with structured business data.

Examples include:

Natural-language analytics on warehouse data
AI-driven reconciliation and validation
Analytical copilots
Data quality automation
Business metric validation
Automated anomaly detection
Multi-agent analytical workflows

The focus is not AI for the entire company. The focus is specifically AI working with analytical and financial data inside the data warehouse ecosystem.

1. Build AI-Ready Data Warehouse Solutions

Design analytical data structures that can be reliably consumed by both humans and AI systems. Move business logic into the warehouse, improve semantic consistency, define reusable metrics, and ensure analytical correctness across systems.

2. Design AI Interactions with Warehouse Data

Help define how AI interacts with warehouse data through prompts, semantic context, retrieval logic, validation mechanisms, and AI workflows. Ensure AI produces correct and trusted answers.

3. Work with LLMs & AI Agents

Work hands-on with technologies such as ChatGPT, Claude, OpenAI APIs, and AI agents. Test, evaluate, and improve AI outputs. Design workflows where multiple AI agents collaborate to solve analytical problems.

4. Ensure Metric Correctness

Help define how AI interprets business metrics and financial logic. Ensure AI correctly understands the difference between concepts such as revenue, cost, gross revenue, net revenue, trading volume, cash movement, accounting movement, balances, and position changes.

5. Improve Data Governance & Reliability

Ensure AI interacts with data in a controlled and secure manner. Help implement validation mechanisms, prevent sensitive data exposure, and improve trust in AI-generated outputs.

Data Platform

Build clean, reusable analytical data layers in BigQuery
Move business logic from BI into the warehouse
Define metrics, dimensions, and semantic consistency

AI Interaction

Enable reliable AI ↔ data interaction
Design schemas + instructions so AI produces correct outputs
Test and refine real AI usage (not theory)

Access & Governance

Implement data-layer access control (not BI-layer)
Row/column-level security, role/attribute-based access
Ensure consistent behavior across BI, AI, and internal tools
Ensure metric reconciliation across different data sources

Prevent:

sensitive data leakage
shadow metric layer
uncontrolled query cost

Automation:

Replace manual data workflows with AI-driven processes
Build agents for reporting, validation, and internal analytics

Strong SQL and analytical data modeling
Experience working with analytical databases (BigQuery, Snowflake, Databricks, Redshift or similar)
Practical experience with LLMs (ChatGPT, Claude, OpenAI APIs or similar)
Experience building AI-driven workflows or agent-based systems
Strong analytical and problem-solving skills
Understanding of machine learning fundamentals